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Found 9 papers, 3 papers with code
QUBIQ: Uncertainty Quantification for Biomedical Image Segmentation Challenge
The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets.
Generating Handwritten Mathematical Expressions From Symbol Graphs: An End-to-End Pipeline
To alleviate this challenge we propose a novel end-to-end G2I generation pipeline (i. e. graph - layout - mask - image) which requires no real masks or nondifferentiable alignment between layouts and masks.
Graph Convolutional Neural Networks with Diverse Negative Samples via Decomposed Determinant Point Processes
However, there are more non-neighbour nodes in the whole graph, which provide diverse and useful information for the representation update.
Learning from the Dark: Boosting Graph Convolutional Neural Networks with Diverse Negative Samples
An interesting way to understand GCNs is to think of them as a message passing mechanism where each node updates its representation by accepting information from its neighbours (also known as positive samples).
Repulsive Mixture Models of Exponential Family PCA for Clustering
To alleviate this problem, in this paper, a repulsiveness-encouraging prior is introduced among mixing components and a diversified EPCA mixture (DEPCAM) model is developed in the Bayesian framework.
Detecting Communities in Heterogeneous Multi-Relational Networks:A Message Passing based Approach
Specifically, an HMRNet is reorganized into a hierarchical structure with homogeneous networks as its layers and heterogeneous links connecting them.
Diversified Hidden Markov Models for Sequential Labeling
While the first-order Hidden Markov Models (HMM) provides a fundamental approach for unsupervised sequential labeling, the basic model does not show satisfying performance when it is directly applied to real world problems, such as part-of-speech tagging (PoS tagging) and optical character recognition (OCR).
Adapting Stochastic Block Models to Power-Law Degree Distributions
Stochastic block models (SBMs) have been playing an important role in modeling clusters or community structures of network data.
Conditional Graphical Lasso for Multi-Label Image Classification
Multi-label image classification aims to predict multiple labels for a single image which contains diverse content.
